System and Method for Planning and Generating Queries for Multi-Dimensional Analysis using Domain Models and Data Federation

ABSTRACT

Data integration and data analysis using computing equipment, software as well as hardware, includes a system and method for integrating data from various data sources, structured and unstructured, without physically creating a data warehouse and automatically generating queries for analysis of the integrated data from a multitude of different views.

FIELD OF THE INVENTION

The present invention relates to data integration and data analysisusing computing equipment, software as well as hardware, and, moreparticularly, to a system and method for integrating data from variousdata sources, structured and unstructured, without physically creating adata warehouse and automatically generating queries for analysis of theintegrated data from a multitude of different views.

BACKGROUND OF THE INVENTION

In recent years, industry trends toward mergers and acquisitions hasforced most OEMs to re-think their processes to account for themultitude of data sources used by the various corporate divisions withinthe enterprise for storing and manipulating product data, including theproduct bill of materials, parts catalog, diagnostic procedures andwarranty claims as a partial list of the kinds of product data thatexists.

Government regulations, rising production cost, shorter time-to-marketare yet other reasons why companies are looking for a more adaptive anddynamic information technology (IT) infrastructure that would scale tothe on-demand era for product life cycle management.

The complexity of the IT environment in the industrial manufacturingsector has grown exponentially over the years creating problems. Someattributes of these problems include:

The increasingly complex nature of the product itself as measured by thenumber of components that goes into the making of a product and thenumber of configurations for each product. This translates intoincreased storage and processing capacities for managing the informationassociated with the product design that could be in the order of aterabyte for just one product model.

The lack of visibility across the product life cycle due to thedisparity of data management systems used in the different stages of thedesign, development, manufacturing process and services after sales. Alarge number of heterogeneous systems that are in use throughout theextended enterprise create an artificial barrier for informationsharing.

The product design is often organized in silos around specific productassemblies (in the case of automobiles, e.g., wing design, enginedesign, body structure design, interior design, etc) making it difficultto integrate data across multiple divisions. While the project team isoften composed of a multi-disciplinary group of engineers, the IT toolsremain too fragmented and specifically tailored to the organization ordivision that uses them the most.

In this environment, traditional approaches for data integration usingdata warehousing does not always scale well. A data warehouse is a copyof transaction data specifically structured for querying, analyzing, andreporting. A data warehouse can be normalized or denormalized. It can bea relational database, flat file, hierarchical database, objectdatabase, etc. Data warehouse data often gets changed. Data warehousesoften focus on a specific activity or entity. Data warehouses areusually designed to meet the requirements of one specific applicationand are not easily extensible without tearing down and rebuilding thetable schema. They provide a fixed view on the data and are not easilyadapted to changing business needs such as when new suppliers areintegrated into the value chain.

Furthermore, product data tends to be deeply hierarchical in nature andhas associated semantics and access control procedures that areencapsulated within the data management system that hosts theinformation and cannot be easily exposed to external applications. Inorder to safeguard the integrity of the data that is owned by any givenpartner, the industry had traditionally resorted to product dataexchange where each partner exports a subset of the data that is storedwithin its domain and shares the data with other partners by mean ofdata replication. This approach tends to be slow and costly as multipleiterations may be required to provide the information that is needed.The approach leads to data redundancy where multiple replicas of thesame work product could exist within the extended enterprise andrequires additional complexity for managing the life-cycle of theexchanged information.

As industry transforms its processes to better leverage the resourcesand know-how of the extended enterprise, a new approach based on datafederation emerges as it promises to deliver on speed and accuracy, bothof which are needed to quickly predict and pinpoint weaknesses in aproduct design and performance. Delivering on such a promise requires abetter understanding of the semantic and data models that areprominently used in the industry.

In the following description the automotive industry will be used as anillustrative example of an application of the present invention.However, the invention is not to be construed as being limited solely tothe automotive industry. A generic product structure is a hierarchicalstructure of generic concepts or functions such as the vehicle bodystructure or the vehicle hydraulic system. The generic product structuredescribes a logical aggregation of the vehicle assemblies and serves asa template for creating the detailed product structure. As such, thegeneric product structure can be used to define the common concepts(e.g., seats) that are shared among similar product classes (e.g., SUVand passenger cars).

An ontology is a specification of a conceptionalization. That is, anontology is a description (like a formal specification of a program) ofthe concepts and relationships that can exist for an agent or acommunity of agents. A common ontology defines a vocabulary with whichqueries and assertions are exchanged among agents. A commitment to acommon ontology is a guarantee of consistency, but not completeness,with respect to queries and assertions using the vocabulary defined inthe ontology. An automotive vehicle ontology is an annotated meta modelof the generic product structure, and processes that can execute againstsuch structure. It augments the generic product structure with variousrelationships and dependencies that may exist between the differentcomponents but cannot otherwise be expressed in the generic productstructure or the detailed product structure. For example, the genericproduct structure for a vehicle may contain a placeholder for the wheelassembly. The vehicle ontology augments this assertion by defining a“similar to” relationship between the wheel assembly of a sports sameproduct class. One derived benefit of such ontological relation is tobroaden the scope of the search to a wider set of data sources thatotherwise would not have been considered.

The vehicle ontology provides the foundation for defining a commonsemantic model of the product structure with all the associatedengineering processes that execute against the product structure asshareable business objects. FIG. 15 is a block diagram of one possiblevehicle ontology.

Reducing warranty costs by conducting a deep failure analysis andimproved claim processes have been identified as a strategic initiativeby many in the automotive industry. While OEMs continues to strive tomanage warranty payout while improving supplier recovery for failedparts, they recognize the value of proactive failure identification. Theobjectives of these efforts are to reduce the cost of warranty throughidentification of warranty issues more quickly than has been previouslyachieved and thereby reducing costs; and enhancement of brand loyalty bydemonstrating a commitment to the reliability and quality of productscarrying the brand name.

Earl warning and failure analysis solutions focus on applying datamining and analytics against a wider set of data sources includingwarranty claims, call-center contacts, vehicle bills of materials,supplier parts catalog, suppliers' bulletins and other attributes forhow and where the vehicle is used. The analytics aims at identifyingtrends, patterns, and abnormalities at an early stage and creating aknowledge model that can be used by the quality engineers to anticipateany major recall.

The present invention provides a system and method for planning andgenerating queries for multi-dimensional analysis across divisions in anentity using domain models and data integration.

SUMMARY OF THE INVENTION

A principal object of the present invention is therefore, the provisionof a system and method for integrating (federating) data from variousdata sources, structured and unstructured.

An object of the present invention is the provision of a system andmethod for executing human-friendly queries over integrated datasources.

Another object of the present invention is the provision of a system andmethod for automatically planning and generating physical queries tointegrated data sources from human-friendly queries.

A further object of the present invention is the provision of a systemand method for analyzing integrated data sources from a multitude ofviews and dimensions without physically building a data warehouse and/orOLAP (data warehouse for analysis processing) data model (e.g., a staror snow-flake schema), which are traditional approaches tomulti-dimensional analysis.

A still further object of the present invention is the provision of asystem and method for creating semantic models for a domain for dataintegration and analysis.

A yet further object of the present invention is the provision of asystem and method for generating and recording mappings between thesemantic model and data sources.

A still another object of the present invention is the provision of asystem and method for using a data federation system for structuring theaccess of data from unstructured data sources.

A yet another object of the present invention is the provision of asystem and method for using a report system for generating reports formulti-dimensional analysis.

Further and still other objects of the present invention will becomemore clearly apparent when the following description is read inconjunction with the accompanying drawing.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 shows a semantic model for automotive diagnostics.

FIG. 2 shows an expanded semantic model for automotive diagnostics.

FIG. 3 shows a semantic model with mapping to data sources.

FIG. 4 shows a mapping of semantic model between a semantic model and anIT model with data sources.

FIG. 5 shows schematically a method for generating a SQL query.

FIG. 6 shows schematically relationship types of classes in a semanticmodel.

FIG. 7 shows a flow chart of a mapping algorithm.

FIG. 8 shows a flow chart of a system architecture overview.

FIG. 9 shows semantic query and equivalence class.

FIG. 10 shows a semantic query and subclass and a superclass.

FIG. 11 shows semantic query and transitive property.

FIG. 12 shows semantic query and symmetric property.

FIG. 13 shows semantic query and inverse properties.

FIG. 14 is a schematic block diagram of an ontology query server shownin FIG. 8.

FIG. 15 is schematic block diagram of an ontology model for a vehicle.

DETAILED DESCRIPTION

Referring now to the figures and specifically to FIGS. 1 and 2 there areshown a semantic model for automotive diagnostics and an expandedsemantic model, respectively. A semantic model (ontology) is a formalexplicit description of classes, i.e., concepts in a domain of discourse(e.g., automotive), properties of each class describing its attributes,relations with other classes being a special kind of properties (e.g.,subclass, equivalentClass, etc.), properties of relations (e.g.,transitive, symmetric, inverse, etc.), and constraints on properties(e.g., cardinality, etc.). Knowledge base often indicates a semanticmodel together with a set of individual instances of classes. Thetheoretical foundation of semantic models and technologies include logic(First Order Logic and Description Logic), knowledge representation ofartificial intelligence (AI), and symbolic computation. The advantagesof using semantic models include: (1) the models provide a means toexpress rich semantics of concepts and relations of domains, and (2) themodels provide a means to query knowledge base with automated reasoning(inferencing).

FIG. 1 and FIG. 2 show a semantic model and an expanded semantic modelrelated to an automotive diagnostic domain. It is to be understood thatthe automotive example is provided for illustrative purposes but theinvention is not so limited. The invention is applicable for anyapplication. At the center of the model, there is a concept representinga failure code 100. The failure code concept is connected via edges tovarious other concepts of the domains which contain information usefulfor identifying and isolating problems related to the failure code,including factory 102, dealer 104, car model 106, warranty 108, platform110, bill-of-materials 112, component 114, part 116, supplier 118, anddriver location 120. The model includes also generic concepts such astime 122. FIG. 1 shows only a partial model. It can be expanded invarious ways by adding more related concepts and their properties. Forinstance, the model can be extended with a set of geography-relatedconcepts that will be connected to the driver location concept 120.Also, different properties and constraints on classes and propertiessuch as subClass, equivalentClass, transitive, symmetric, inverse, andcardinality can be used to represent various semantics. In FIGS. 1 and 2time and driver location are expressed to have hierarchical structures.

FIG. 3 shows a semantic model with mapping to data sources: Theusefulness of semantic models themselves is limited because a modelitself does not mean much without instances it represents. However, whensemantic models are connected to various data sources which provideinstances of concepts represented in the semantic models, they becomevery useful for various purposes. The models can be used to integratevarious data sources; provide a means for users to use human-friendly,semantic queries to access information from the integrated data sources;automatically translate human-friendly, semantic queries to physicalqueries to real data sources underneath; and help users analyzeinformation from the integrated data sources.

The first step in realizing all these benefits is to create a mappingbetween the semantic model and the underlying data sources.Unfortunately, this task is not straightforward, because often thestructures of data sources vary drastically. It is also necessary tohandle a multitude of data sources in an enterprise environment.Moreover, some sources do not have much structure (e.g., semi-structuredor unstructured data). The mapping creation step is described below inconjunction with FIG. 7.

FIG. 3 shows the semantic model of an automotive domain with mappinginformation to data sources. In the figure each oval, i.e., concept, isassociated with a respective box which represents a mapping to one ormore columns in one or more tables (or table views) in one or morerelational databases.

FIG. 4 shows in more detail examples of the mapping information betweenthe semantic model and the IT model (e.g., relational tables, flatfiles, spreadsheets, XML files, etc.). The semantic model is shown onthe left-hand side of the figure. The right-hand side of the figurerepresents the semantic model associated with mapping information. Eachrelation of each class in the semantic model is associated with a boxwhich represents one or more columns in one or more tables (or tableviews) in one or more relational databases. This mapping information isused to generate physical queries from semantic queries submitted byhuman users. For example, in the semantic model a customer 400 is sold avehicle 402 which was manufactured at assembly plant 404 on build date406. In the semantic model associated with the mapping information,vehicle 402 has associated with it a specific vehicle identificationnumber 402A and warranty VIN number 402B. The plant 404 has associatedwith it a specific VIN number 404A for the vehicle 402, such as aparticular assembly line where the vehicle was assembled.

FIG. 5 shows a method for generating a SQL query illustrating an exampleof a semantic query and how the semantic query is translated into aphysical query to a data source by using the mapping informationassociated with concepts and relations of the semantic model. Theleft-hand side of FIG. 5 corresponds to the right-hand side of FIG. 4.When an event such as a Fail Code=12abc 500 occurs, an initial query 502is made “how is Fail Code related to Plant?”. The result 504 is the VINnumber of the plant where the Fail Code was generated.

FIG. 6 shows relationship types that each relation of each class in thesemantic model, such as vehicle 600, is associated with boxes forvehicle identification number 602, and warranty identification number604, which represents one or more columns in one or more tables (ortable views) in one or more relational databases. This mappinginformation is used to generate physical queries from semantic queriessubmitted by human users.

FIG. 7 is a flow chart of a mapping algorithm. One method of (semi-)automating the mapping process between semantic models and IT models isto start with utilizing similarities in names in the domain model 700and IT model 702. For example, if the domain model has a concept namedphone number and the IT model has columns named phone, phone_num,phone_number, pnum, etc., there is a high chance that these columns canbe mapped to the phone number concept in the domain model. However, thesystem should not map them automatically without human intervention.Rather, the similarity mapping manager 704 would suggest possiblemapping of the concept and columns to the user 706, and let the userconfirm the final mapping 708. In order to help identify possiblemapping among variations of a same concept, it is possible to use someexisting lexical database 710 such as WordNet, which provides sets ofsynonyms, acronyms, and other linguistic variations 712 to the query714. If necessary, the system can provide a facility to add more suchsets to the lexical database.

Once the initial mapping 716 is bootstrapped by utilizing namingsimilarities, then the system can make further mapping suggestionsregarding neighboring concepts in neighbor mapping manager 718. Forexample, once the phone number concept in the domain model is mapped tothe pnum column in a table in the IT model, the system can suggest thatthe address concept neighboring with the phone number concept be mappedto the addr column in the table. Again, the system would suggest themapping 720 and a human makes the final mapping decision. In this way,the system can incrementally build mapping information by using priormapping and human interaction. Also, after the mapping process, the usercan add more semantics, if necessary, to the classes and properties inthe model, such as symmetry, transitivity, inverse, etc. by means of anannotation manager 722. The final mapping yields a new domain model 724and IT model 726.

FIG. 8 is a flow chart of the system architecture overview showing theend-to-end steps of how a preferred embodiment of the invention worksfor the users. The Users 800 of the system are typically informationanalysts, for example, in the domain of automotive diagnostics, claimanalysts who want to understand a particular set of automotive failuresand how they are related with other dimensions of the automotive domain,e.g., warranty, manufacturing, assembly, geography, and the like. Theuser submits queries to find answers to such questions. Initially thequery 802 is a natural language free form query that is not constrainedby any form of underlying data stores. An ontology query generator 804translates the human query to semantic queries that can be understood bythe semantic model (domain model) 806. Still, the semantic query is notconstrained by data store forms. The SQL query plan generator 808 usesthe semantic model (domain model) 806 and the mapping information frommapping server 805 associated with concepts and relations of the domainmodel to generate a SQL query that is understood by relational databasesources 810, 812. Not all the underlying data stores may be relationaldatabase stores that understand SQL query. A data federation system 814such as IBM DB2II (Information Integration) solves that problem bymaking non-relational, semi-structured, unstructured data stores looklike relational databases and understand SQL queries. Softwarecomponents doing this job are often referred to as adaptors 816 and 818.

The database sources are located on one or more servers oralternatively, are located on the World Wide Web.

As explained above, one of the advantages of using semantic models isthe ability to express rich semantics of concepts and relations, and usethe semantics in answering queries providing automated reasoning andinferencing (based on logic). FIGS. 9 to 13 show some examples of suchreasoning with semantic queries. The ontology query server supports theautomated reasoning capability.

The query result returned by the data sources is federated by the datafederation system 814, and then passed to the report generator 820,which can generate reports regarding various useful multidimensionalanalyses.

The mapping server 805 is a build-time tool described in conjunctionwith FIG. 7, which is used to create semi-automatically the mappinginformation between semantic models and IT models. The mapping serveridentifies tables/views (and columns defined in them) in the IT modelthat present relations in the domain model. It is possible to (semi-)automate the process by using some heuristics, machine learning and/orstatistical approaches. The semantic model may be defined in terms of adomain tree. Also, if is possible to define some simple rules forcreating joins when necessary.

The user (analyst) submits the query through Query GUI 802. The OntologyQuery Generator 804 translates the submitted query to an ontology queryin server 822 in N3 format. An example of a user query is as follows:

Show me all the dimensions of Failure Code.

And an example of an ontology query is as follows:

(FailureCode, hasDimension, ?X).

The Ontology Query Server 822 processes the query and returns a ResultSet:

?X=Time, Driver Location, Dealer, Component, Car Model, Factory

The Result Set is used by the SQL Query Plan Generator 808 to compose aSQL query. The Result Set can be shown to the user for selectingdimensions of interest for the composition.

The SQL Query Plan Generator 808 composes a SQL query by using theResult Set from Ontology Query Server 822 and Mapping information fromMapping Server 805. An example of a SQL query is as follows:

SELECT COUNT(FailureCode), Time, Component, CarModel, Factory

FROM table_list

WHERE FailureCode=“XX” and join_conditions

[GROUP BY CarModel]

The GROUP BY dimension can be any dimension from the list. The querybasically creates a cube view over aggregated counts of the givenFailure Code. The query is submitted to data sources 810 and 812 via theData Federation System 814 which retrieves data instances from the datasources. The retrieved data instances are displayed as a report by theReport Generator 820 (e.g., Alpha Blocks). The analyst reviews thisreport and decides on the next query, repeating the above steps.

Drill-down and roll-up is an important query type of OLAP along withaggregation. Ontology query can help compose drill-down and roll-upqueries.

A user query example is as follows:

Snow me all the granularities of Time class

An ontology query is:

(Time, hasComponents, ?X)

A Result Set is:

?X=Year, Month, Week, Day, Hour, . . .

The SQL Query Plan Generator 808 composes a SQL query by using theResult Set and Mapping information. The SQL query is:

SELECT COUNT(FailureCode), Month, Component, CarModel, Factory

FROM table_list

WHERE FailureCode=“XX” and join_conditions

GROUP BY Month

The query is submitted to data sources 810 and 812 via the DataFederation System 814 (DB2II), which retrieves the data on the fly. Notethat in traditional OLAP systems all the aggregation values arepre-computed.

The user continues the analysis by finding classes and data directlyrelated to the given Failure Code. A user query example is:

Show me all the secondary dimensions of Failure Code

An ontology query is:

(FailureCode, hasDimension, ?X) (?X, hasDimension, ?Y)

A Result Set is:

?X=Time, Driver Location, Dealer, Component, Car Model, Factory

?Y=Warranty, Platform, Bill Of Material, Part

The secondary dimensions can be used by the SQL Query Plan Generator 808to compose a SQL query. The secondary dimensions along with directdimensions can be shown to the user for selecting dimensions of interestfor the composition. The SQL Query Plan Generator composes a SQL queryby using the Result Set and Mapping information. A SQL query is:

SELECT COUNT(FailureCode), Time, Warranty, Platform, Part

FROM table_list

WHERE FailureCode=“XX” and join_conditions

GROUP BY Warranty

The user can simply move the focus of the analysis to a different class.A user query example is:

Show me all the dimensions of Component

An ontology query is:

(Component, hasDimension, ?X)

A Result Set is:

?X=Failure Code, Part, Bill Of Material

The SQL Query Generator composes a SQL query by using the Result Set andmapping information. A SQL query is:

SELECT COUNT(FailureCode), Component, Part

FROM table_list

WHERE join_conditions

GROUP BY Part

FIG. 9 shows a semantic query and equivalence class. The presentinvention supports semantic queries by utilizing a “semantic network”for defining a domain model 900. Semantic networks specify relationshipsamong concepts in the model and use the meaning (semantics) of therelationships in answering queries against the model. Examples of therelationships include generalization (superclass), specification(subclass), equivalence, symmetry, transitivity, and inverse propertiesof relationships. Certain prior art networks, such as Google and Yahoo,do not support such semantic queries.

FIG. 9 shows an example of an equivalence class. Suppose the user wantsto find information (e.g., styles) about automobiles 902. Prior artsystems yield a result which will display information only onautomobiles 902. The present invention yields a result which willdisplay information on automobiles 902 and vehicles 904, assuming thedomain model defines automobile and vehicle to be equivalent concepts.

FIG. 10 shows a semantic query and subclass and a superclass. Supposethe user wants to find phone number 1002 of a customer. Prior artsystems look for only a phone number column in the table; and the resultis a null if no such column exists in the table. The present inventionlooks for phone number, home phone number, office phone number, andmobile phone number to answer the query, assuming the domain modeldefines home/office/mobile phone numbers to be subclasses of phonenumber.

FIG. 11 illustrates a semantic query and transitive property. Assume theuser wants to find regions located in New York. The semantic model 1100can define the locations Yorktown 1102, Hawthorne 1104, Westchester1106, and New York 1108 which are transitive properties. The data storesgive instances such as Yorktown which is located in Westchester Countyand Hawthorne which is also located in Westchester County. WestchesterCounty is located in New York. Then the query result returns not onlyWestchester but also Yorktown and Hawthorne because of the transitivityproperty.

FIG. 12 shows a semantic query and symmetric property. Assume the userwants to find equivalent classes of vehicles. Further assume that noneof the data stores has this information explicitly. Also, assume thatthe semantic model 1200 defines that equivalence is a symmetric property1202. Then, assuming the fact that automobile 1204 is equivalent tovehicle 1206 is defined in the semantic model 1200 as shown in FIG. 9,the new fact that a vehicle is equivalent to automobile is inferred fromthe semantic model by using that the knowledge equivalence is asymmetric property. Therefore, the query result returns automobile,because it is reasoned to be equivalent with vehicle.

FIG. 13 shows semantic query and inverse properties. Suppose the userwants to find a child of John when the underlying data stores only keepthe fact that John 1302 is the father of Fred 1304. A query to find achild of John to the data stores cannot find an answer. The semanticmodel 1300 can be used to interpret the query. If the semantic modeldefines that “child of” is an inverse property of “father of” 1306, thenan implied fact that Fred is child of John can be found. The queryresult returns Fred, because Fred is reasoned to be the John's child.

FIG. 14 is a schematic block diagram of ontology query server 822. Auser 800 submits a query to user interface 1402. An application 1404 issubmitted to an application programming interface (API) 1406. The userinterface 1402 is also connected to API 1406. Ontology management system1408 contains ontology create unit 1410, ontology edit unit 1412,ontology translation unit 1414, ontology store unit 1416, ontology queryunit 1418, query optimization unit 1420, and ontology directory 1422. Inaddition, the system 1408 includes a working memory 1424 and a rulesbase 1426. Moreover, ontology persistent store 1428 memory is containedin system 1408. An ontology load unit 1430 is connected to ontologysource connector 1432 which, in turn, provides as the system outputontology files 1434. In other words, the units within system 1408interoperate to process user query inputs according to the selectedapplication and ontology rules base in order to provide the ontologyfiles output.

It will be understood by those skilled in the art that while the abovedescription refers to the automotive industry in examples in describingthe invention, the invention is not so limited and is applicable to anysituation where there is a use for planning and generating queries formulti-dimensional analysis of data.

While there has been described and illustrated a system and method forplanning and generating queries for multi-dimensional analysis usingdomain models and data federation, it will be apparent to those skilledin the art that modifications and variations are possible withoutdeviating from the broad teachings and spirit of the present inventionwhich shall be limited solely by the scope of the claims appendedhereto.

1. A computer server search engine capable of operating with one or morecomputing equipment, the server search engine comprising: a queryreceiving processor for receiving a query containing one or more termsand one or more semantic attributes; and a query response processor forproviding a result to the query, the result containing at least onesemantic term related to one or more of the semantic attributes.
 2. Acomputer search engine as set forth in claim 1, where the semanticattributes are selected from the group consisting of subset, superset,subclass, superclass, similarity, symmetry, transitivity, inverse,equivalence, cardinality and any combination thereof.
 3. A computerserver search engine as set forth in claim 1, further comprisingdatabases containing information.
 4. A computer server search engine asset forth in claim 3, where said databases are located on a server.
 5. Acomputer server search engine as set forth in claim 3, where saiddatabases are located on the World Wide Web.
 6. A computer server searchengine as set forth in claim 3, where the databases are arranged in oneor more hierarchies, each hierarchy comprising semantic concepts of adomain and edges representing a semantic attribute connecting concepts.7. A computer server search engine as set forth in claim 6, wheresemantic attributes are defined by a domain tree.
 8. A computer serversearch engine as set forth in claim 6, where a semantic term is definedby one or more edges and a concept is identified by one or more semanticterms.
 9. A computer server search engine as set forth in claim 6, wheresaid query response processor operates on a sub-hierarchy of ahierarchy.
 10. A computer server search engine as set forth in claim 6,where said query response processor operates on a super hierarchycreated by joining two or more hierarchies.
 11. A computer server searchengine as set forth in claim 1, wherein the query receiving processorand the query response processor utilize a data structure arranged inone or more hierarchies, the hierarchies comprising: semantic conceptsof a domain and edges representing the semantic attributes connectingconcepts. 12-25. (canceled)